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SBIR Phase II:Composing Digital-Twins from Disparate Data Sources

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2321894
Agency Tracking Number: 2321894
Amount: $999,757.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AV
Solicitation Number: NSF 23-516
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-10-01
Award End Date (Contract End Date): 2025-09-30
Small Business Information
15714 Crestbrook Dr.
Houston, TX 77059
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Blackwell
 (713) 730-9909
Business Contact
 John Blackwell
Phone: (713) 730-9909
Research Institution

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project relates to the creation of a digital twin, an interactive, 3-dimensional model of a real-world system, of complex industrial environments and assets. This digital twin provides infrastructure necessary for the application of virtual reality training and augmented reality live-guided procedures in industrial workplaces, at scale. By making the full-scale roll-out of these technologies possible, the technology seeks to impact human health and safety, operational efficiencies, and environmental risk reduction for process operations facilities, such as oil refineries and chemical plants. The long-term impacts of this technology may also enable automation and optimization, improving their efficiency, security, and safety. Such facilities are critical infrastructure and play a significant role in the national economy. The availability of this product may also enhance market opportunities for other businesses in the scanning, spatial computing, and training markets. The impact may be further broadened by adapting the process for digital twin production to new domains unrelated to the industrial market._x000D_
This Small Business Innovation Research (SBIR) Phase II project is advancing knowledge and understanding in both machine learning and spatial computing. This project focuses on a method for digitizing a complex, real-world system, in an efficient manner, sufficient to recreate the captured reality as an interactive digital twin. The primary technical hurdle is the combining of different data sources, that describe aspects of a particular real-world system, into a single, complete description. The initial physical systems being modelled are industrial process operations, but the core methods could apply to other types of systems, including natural systems, such as a rainforest. For industrial process operations, the goal is to encode the entire process operations facilities, at the component level, with sub-centimeter accuracy, at 10% of the current time and cost requirements. To achieve this, this project will combine physical scans with engineering documentation and relational probabilities. Once combined, the model will be used as the basis for a digital twin of the real-world system projected into spatial computed environments, such as virtual and augmented reality. These techniques replace a tedious and limited static scan and intensive human labor workflow with rapid scans, computer vision, and a combination of procedural and trained algorithms._x000D_
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

* Information listed above is at the time of submission. *

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